By way of introductory example, consider that most common of debt instruments, the consumer mortgage. The value of a mortgage depends in large part on the duration of the mortgage. At the inception of the mortgage there are broker fees and various other settlement costs that are charged to the lender. When a mortgage extends for the term of many years, there is an opportunity for the lender to recoup costs of putting a mortgage in place for a given consumer and to make profit on that mortgage. This is particularly important for all business organizations that lend money, but it is particularly important for those mortgage financing organizations which have stockholders and other investors.
When a mortgage loan is paid off early due to refinancing, depending upon how early in the term, there is the possibility that the lending institution can actually take a loss on the particular mortgage. The rate of prepayment depends on a number of objective factors. For example, during times of decreasing mortgage rates, on average, more consumers refinance their home loans than would otherwise occur, in order to obtain a lower monthly payment. However, for a given macroeconomic environment and other measurable, objective factors, each consumer evidences an individual propensity to prepay a loan. This prepayment propensity reflects the consumer's demographic and other objective attributes. A system that can assess such individual prepayment behavior by a consumer in advance of the loan will lead to more profitable loans being made, and hence the enhanced availability of funds for loans to more consumer-borrowers. The present invention therefore may be applied without limitation to a) the pricing of mortgages and other debt instruments, b) the valuation of existing portfolios of debt instruments, and c) the risk management of institutions that hold debt instruments.
A further element of the present invention is the monitoring and scoring of mortgage brokers. Mortgage brokers deal with both consumer-borrowers and lenders-clients. In order to generate brokerage fees, it is possible for a broker to encourage its consumer-borrowers to refinance their mortgages frequently and prematurely. When this occurs, the mortgage broker generates a fee. However, early prepayment of the prior mortgage instrument can result in a loss for the lender. Thus the present invention also has the capability to score mortgage broker prepayment behavior.
The behavior of a broker is sometimes not all heinous. Sometimes a consumer, who is particularly attuned to the rise and fall of interest rates, will simply be the one who changes mortgage instruments more frequently than the average consumer. The broker who is scored based upon the prepayment behavior of the consumers that the broker brings to lenders, would like to know the prepayment propensity for the given consumer. This would be useful so that the mortgage broker can optimize the broker's relationship with its lender-clients by only bringing consumer-borrowers who have a low prepayment propensity.
Therefore, lenders and brokers badly need the ability to better measure prepayment behavior in advance of incurring marketing or underwriting charges; these expenses are too great to absorb blindly on behalf of consumers with poor prepayment propensities. Indeed, a beneficial use of the invention would be in managing the initial marketing effort itself. For example, only those customers who can be shown to score favorably for prepayment behavior might receive a solicitation for a mortgage product A. Consumers who are revealed to represent a substantial prepayment risk may be offered a more suitable mortgage product B, reflecting the increased risk. In this way, enhanced customer segmentation and product design initiatives converge to benefit consumers and their sources of debt financing.
To understand the potential impact of a national prepayment scoring standard, as manifested in the present invention, one need look no farther than the existing default risk scoring standard, owned and distributed by Fair, Isaac and Company, Inc. (Fair Isaac) for over 30 years. By establishing a standard methodology for scoring borrower default risk, and broadly disseminating it, Fair Isaac dramatically enhanced mortgage lender insight into expected loan dynamics. In finance, enhanced insight is synonymous with enhanced information. Enhanced information implies reduced risk for the lender. Finally, reduced lender risk profiles produce lower costs of capital. In other words, because Fair Isaac standardized successfully a fungible measurement of default risk, more money is available for consumers to borrow, at better and cheaper interest rates. The market is more efficient than before and everyone benefits.
To further qualifying the timeliness of the invention, please refer to exhibit 1, "Green Tree chief returns $23 million . . . "The Wall Street Journal, March, 1998. This story highlights the industry wide uncertainty surrounding prepayment speeds in consumer debt portfolios. One industry leading company, Green Tree Financial, "has been hit hard the past year by escalating loan losses in the painful recognition that its accounting has been too aggressive. Also, an unexpected wave of loan prepayments hit the industry, as borrowers sought lower interest rates, indicating working-class consumers were not as unsophisticated as lenders had believed." Stated plainly, Green Tree overstated prior year earnings significantly, exercising its option under GAAP accounting to roll forward and capture in advance projected lending profits, even though those very profits were merely estimated based in part on arbitrary prepayment assumptions. In large measure because Green Tree badly miscalculated these prepayments speed assumptions, in 1997 the company was forced to charge off $390 million of 1996 reported profit. In 1998 the company was sold off to Conseco.
Earlier disclosures in the area of prepayment scoring in a lending context are limited or nonexistent. U.S. Pat. No. 5,696,907, entitled "System and Method for Performing Risk and Credit Analysis of Financial Service Applications," issued to Tom. The Tom patent discloses using a neural network to mimic a loan officer's underwriting decision making. The method of the Tom patent is based on a non-iterative regression process that produces an approval criterion that is useful in preparing new or modified underwriting guidelines to increase profitability and minimize losses for a future portfolio of loans. A prepayment observation is used in the neural net as a negative flag, but no prepayment scoring system is utilized in the Tom patent.
In view of the prior art, there is a clear need for measuring and predicting a consumer's prepayment propensity, as well as a clear and strong need for a method and apparatus to produce such a measuring and predictive parameter.